Presentation
13 March 2024 Enabling longitudinal quantitative photoacoustic mesoscopy of vascular networks through image co-registration
Author Affiliations +
Abstract
Longitudinal mesoscopic photoacoustic imaging of vascular networks requires accurate image co-registration to assess local changes in growing tumours, but remains challenging due to sparsity of data and scan-to-scan variability. Here, we compared a set of 5 curated co-registration methods applied to 49 pairs of vascular images of mouse ears and breast cancer xenografts. Images were segmented using a generative adversarial network and pairs of images and/or segmentations were fed into the 5 tested algorithms. We show the feasibility of co-registering vascular networks accurately using a range of quality metrics, taking a step towards longitudinal characterization of those complex structures.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thierry L. Lefebvre, Paul W. Sweeney, Janek Gröhl, Lina Hacker, Emma L. Brown, Thomas R. Else, Mariam-Eleni Oraiopoulou, Algernon Bloom, David Y. Lewis, and Sarah E. Bohndiek "Enabling longitudinal quantitative photoacoustic mesoscopy of vascular networks through image co-registration", Proc. SPIE PC12842, Photons Plus Ultrasound: Imaging and Sensing 2024, PC128421G (13 March 2024); https://doi.org/10.1117/12.3002744
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KEYWORDS
Image segmentation

Photoacoustic spectroscopy

Deformation

Image processing algorithms and systems

Mathematical optimization

Photoacoustic imaging

Deep learning

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